






"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class 						__UpperCamelCase     (     UpperCamelCase   ,     unittest.TestCase						):
					lowercase_    :				Optional[Any]      =  KandinskyInpaintPipeline
					lowercase_    :				Any      =  ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
					lowercase_    :				List[Any]      =  [
					    """prompt""",
					    """negative_prompt""",
					    """image_embeds""",
					    """negative_image_embeds""",
					    """image""",
					    """mask_image""",
					]
					lowercase_    :				Any      =  [
					    """generator""",
					    """height""",
					    """width""",
					    """latents""",
					    """guidance_scale""",
					    """negative_prompt""",
					    """num_inference_steps""",
					    """return_dict""",
					    """guidance_scale""",
					    """num_images_per_prompt""",
					    """output_type""",
					    """return_dict""",
					]
					lowercase_    :				int      =  False
					@property
					def        UpperCAmelCase__ (							self							:	List[str]   )	->       Union[str, Any]:
											return 32
					@property
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       str:
											return 32
					@property
					def        UpperCAmelCase__ (							self							:	List[str]   )	->       List[Any]:
											return self.time_input_dim
					@property
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       Optional[int]:
											return self.time_input_dim * 4
					@property
					def        UpperCAmelCase__ (							self							:	str   )	->       Tuple:
											return 100
					@property
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       Any:
											lowerCAmelCase   :List[str]						=			XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'   )
											return tokenizer
					@property
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       List[str]:
											torch.manual_seed(0   )
											lowerCAmelCase   :Tuple						=			MCLIPConfig(
											    numDims=self.cross_attention_dim     , transformerDimensions=self.text_embedder_hidden_size     , hidden_size=self.text_embedder_hidden_size     , intermediate_size=37     , num_attention_heads=4     , num_hidden_layers=5     , vocab_size=1005     , )

											lowerCAmelCase   :List[str]						=			MultilingualCLIP(UpperCAmelCase   )
											lowerCAmelCase   :int						=			text_encoder.eval()

											return text_encoder
					@property
					def        UpperCAmelCase__ (							self							:	Optional[int]   )	->       List[Any]:
											torch.manual_seed(0   )

											lowerCAmelCase   :Optional[int]						=			{
											    'in_channels': 9,
											    # Out channels is double in channels because predicts mean and variance
											    'out_channels': 8,
											    'addition_embed_type': 'text_image',
											    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
											    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
											    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
											    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
											    'layers_per_block': 1,
											    'encoder_hid_dim': self.text_embedder_hidden_size,
											    'encoder_hid_dim_type': 'text_image_proj',
											    'cross_attention_dim': self.cross_attention_dim,
											    'attention_head_dim': 4,
											    'resnet_time_scale_shift': 'scale_shift',
											    'class_embed_type': None,
											}

											lowerCAmelCase   :Optional[int]						=			UNetaDConditionModel(**UpperCAmelCase   )
											return model
					@property
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       int:
											return {
											    "block_out_channels": [32, 64],
											    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
											    "in_channels": 3,
											    "latent_channels": 4,
											    "layers_per_block": 1,
											    "norm_num_groups": 8,
											    "norm_type": "spatial",
											    "num_vq_embeddings": 12,
											    "out_channels": 3,
											    "up_block_types": [
											        "AttnUpDecoderBlock2D",
											        "UpDecoderBlock2D",
											    ],
											    "vq_embed_dim": 4,
											}
					@property
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       List[Any]:
											torch.manual_seed(0   )
											lowerCAmelCase   :List[Any]						=			VQModel(**self.dummy_movq_kwargs   )
											return model
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       List[Any]:
											lowerCAmelCase   :Tuple						=			self.dummy_text_encoder
											lowerCAmelCase   :List[Any]						=			self.dummy_tokenizer
											lowerCAmelCase   :Union[str, Any]						=			self.dummy_unet
											lowerCAmelCase   :Dict						=			self.dummy_movq

											lowerCAmelCase   :str						=			DDIMScheduler(
											    num_train_timesteps=1000     , beta_schedule='linear'     , beta_start=0.0_0_0_8_5     , beta_end=0.0_1_2     , clip_sample=UpperCAmelCase     , set_alpha_to_one=UpperCAmelCase     , steps_offset=1     , prediction_type='epsilon'     , thresholding=UpperCAmelCase     , )

											lowerCAmelCase   :Tuple						=			{
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											    'unet': unet,
											    'scheduler': scheduler,
											    'movq': movq,
											}

											return components
					def        UpperCAmelCase__ (							self							:	Tuple     , UpperCAmelCase							:	Optional[Any]     , UpperCAmelCase							:	List[Any]=0   )	->       Optional[Any]:
											lowerCAmelCase   :Tuple						=			floats_tensor((1, self.cross_attention_dim)     , rng=random.Random(UpperCAmelCase   )   ).to(UpperCAmelCase   )
											lowerCAmelCase   :List[Any]						=			floats_tensor((1, self.cross_attention_dim)     , rng=random.Random(seed + 1   )   ).to(UpperCAmelCase   )
											# create init_image
											lowerCAmelCase   :Any						=			floats_tensor((1, 3, 64, 64)     , rng=random.Random(UpperCAmelCase   )   ).to(UpperCAmelCase   )
											lowerCAmelCase   :Union[str, Any]						=			image.cpu().permute(0     , 2     , 3     , 1   )[0]
											lowerCAmelCase   :Dict						=			Image.fromarray(np.uinta(UpperCAmelCase   )   ).convert('RGB'   ).resize((256, 256)   )
											# create mask
											lowerCAmelCase   :Union[str, Any]						=			np.ones((64, 64)     , dtype=np.floataa   )
											lowerCAmelCase   :List[Any]						=			0

											if str(UpperCAmelCase   ).startswith('mps'   ):
																	lowerCAmelCase   :List[str]						=			torch.manual_seed(UpperCAmelCase   )
											else:
																	lowerCAmelCase   :Union[str, Any]						=			torch.Generator(device=UpperCAmelCase   ).manual_seed(UpperCAmelCase   )
											lowerCAmelCase   :Any						=			{
											    'prompt': 'horse',
											    'image': init_image,
											    'mask_image': mask,
											    'image_embeds': image_embeds,
											    'negative_image_embeds': negative_image_embeds,
											    'generator': generator,
											    'height': 64,
											    'width': 64,
											    'num_inference_steps': 2,
											    'guidance_scale': 4.0,
											    'output_type': 'np',
											}
											return inputs
					def        UpperCAmelCase__ (							self							:	List[str]   )	->       str:
											lowerCAmelCase   :List[Any]						=			'cpu'

											lowerCAmelCase   :Tuple						=			self.get_dummy_components()

											lowerCAmelCase   :str						=			self.pipeline_class(**UpperCAmelCase   )
											lowerCAmelCase   :Dict						=			pipe.to(UpperCAmelCase   )

											pipe.set_progress_bar_config(disable=UpperCAmelCase   )

											lowerCAmelCase   :Tuple						=			pipe(**self.get_dummy_inputs(UpperCAmelCase   )   )
											lowerCAmelCase   :str						=			output.images

											lowerCAmelCase   :Optional[int]						=			pipe(
											    **self.get_dummy_inputs(UpperCAmelCase   )     , return_dict=UpperCAmelCase     , )[0]

											lowerCAmelCase   :Optional[Any]						=			image[0, -3:, -3:, -1]
											lowerCAmelCase   :str						=			image_from_tuple[0, -3:, -3:, -1]

											print(f"""image.shape {image.shape}"""   )

											assert image.shape == (1, 64, 64, 3)

											lowerCAmelCase   :int						=			np.array(
											    [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]   )

											assert (
											    np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
											), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
											assert (
											    np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
											), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
					def        UpperCAmelCase__ (							self							:	int   )	->       Any:
											super().test_inference_batch_single_identical(expected_max_diff=3e-3   )




@slow
@require_torch_gpu
class 						__UpperCamelCase     (     unittest.TestCase						):
					def        UpperCAmelCase__ (							self							:	Any   )	->       Union[str, Any]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       Tuple:
											lowerCAmelCase   :Tuple						=			load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'   )

											lowerCAmelCase   :List[str]						=			load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'   )
											lowerCAmelCase   :Dict						=			np.ones((768, 768)     , dtype=np.floataa   )
											lowerCAmelCase   :Union[str, Any]						=			0

											lowerCAmelCase   :Optional[Any]						=			'a hat'

											lowerCAmelCase   :Optional[int]						=			KandinskyPriorPipeline.from_pretrained(
											    'kandinsky-community/kandinsky-2-1-prior'     , torch_dtype=torch.floataa   )
											pipe_prior.to(UpperCAmelCase   )

											lowerCAmelCase   :Optional[int]						=			KandinskyInpaintPipeline.from_pretrained(
											    'kandinsky-community/kandinsky-2-1-inpaint'     , torch_dtype=torch.floataa   )
											lowerCAmelCase   :int						=			pipeline.to(UpperCAmelCase   )
											pipeline.set_progress_bar_config(disable=UpperCAmelCase   )

											lowerCAmelCase   :List[str]						=			torch.Generator(device='cpu'   ).manual_seed(0   )
											lowerCAmelCase	, lowerCAmelCase   :Any						=			pipe_prior(
											    UpperCAmelCase     , generator=UpperCAmelCase     , num_inference_steps=5     , negative_prompt=''     , ).to_tuple()

											lowerCAmelCase   :int						=			pipeline(
											    UpperCAmelCase     , image=UpperCAmelCase     , mask_image=UpperCAmelCase     , image_embeds=UpperCAmelCase     , negative_image_embeds=UpperCAmelCase     , generator=UpperCAmelCase     , num_inference_steps=100     , height=768     , width=768     , output_type='np'     , )

											lowerCAmelCase   :Any						=			output.images[0]

											assert image.shape == (768, 768, 3)

											assert_mean_pixel_difference(UpperCAmelCase     , UpperCAmelCase   )







"""simple docstring"""


import heapq as hq
import math
from collections.abc import Iterator




class 						__UpperCamelCase     :
					def __init__(							self							:	Union[str, Any]     , UpperCAmelCase							:	List[Any]   )	->       List[str]:
											lowerCAmelCase   :Dict						=			str(id_   )
											lowerCAmelCase   :str						=			None
											lowerCAmelCase   :Any						=			None
											lowerCAmelCase   :List[str]						=			[]
											lowerCAmelCase   :List[Any]						=			{}  # {vertex:distance}
					def __lt__(							self							:	Union[str, Any]     , UpperCAmelCase							:	Any   )	->       str:
											return self.key < other.key
					def __repr__(							self							:	Union[str, Any]   )	->       Dict:
											return self.id
					def        UpperCAmelCase__ (							self							:	Any     , UpperCAmelCase							:	Tuple   )	->       List[str]:
											self.neighbors.append(UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	str     , UpperCAmelCase							:	Optional[Any]     , UpperCAmelCase							:	List[Any]   )	->       Optional[int]:
											lowerCAmelCase   :str						=			weight


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						graph[a - 1].add_neighbor(graph[b - 1] )
						graph[b - 1].add_neighbor(graph[a - 1] )
						# add the edges:
						graph[a - 1].add_edge(graph[b - 1]     ,      a__ )
						graph[b - 1].add_edge(graph[a - 1]     ,      a__ )


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :List[str]						=			[]
						for u in graph:
												lowerCAmelCase   :Tuple						=			math.inf
												lowerCAmelCase   :Any						=			None
						lowerCAmelCase   :Dict						=			0
						lowerCAmelCase   :int						=			graph[:]
						while q:
												lowerCAmelCase   :str						=			min(a__ )
												q.remove(a__ )
												for v in u.neighbors:
																		if (v in q) and (u.edges[v.id] < v.key):
																								lowerCAmelCase   :List[str]						=			u
																								lowerCAmelCase   :List[Any]						=			u.edges[v.id]
						for i in range(1     ,      len(a__ ) ):
												a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
						return a


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						for u in graph:
												lowerCAmelCase   :List[str]						=			math.inf
												lowerCAmelCase   :str						=			None
						lowerCAmelCase   :Optional[int]						=			0

						lowerCAmelCase   :Dict						=			list(a__ )
						hq.heapify(a__ )

						while h:
												lowerCAmelCase   :Union[str, Any]						=			hq.heappop(a__ )
												for v in u.neighbors:
																		if (v in h) and (u.edges[v.id] < v.key):
																								lowerCAmelCase   :int						=			u
																								lowerCAmelCase   :List[Any]						=			u.edges[v.id]
																								hq.heapify(a__ )

						for i in range(1     ,      len(a__ ) ):
												yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)


def        UpperCAmelCase      (					):





						'''simple docstring'''








if __name__ == "__main__":
		import doctest

		doctest.testmod()